Managerial Economics

Demand Forecasting

Demand Forecasting

It means expectation about future course of the market demand for a product based on statistical data about past behavior and empirical relationships of demand determinants

Types:Short termLong termPassive & Active Forecasts

Short Term Forecasting

It normally relates to a period not exceeding a year

Benefits of Short term forecastingEvolving a Sales PolicyDetermining Price PolicyFixation of Sales Target

Long Term Forecasting

It refers to the forecasts prepared for long period during which the firm’s scale of operations or the production capacity may be expanded or reducedBenefits of Long term forecasting Business PlanningManpower PlanningLong-Term Financial Planning

Factors involved in Demand Forecasting

Undertaken at three levels:a. Macro-levelb. Industry level eg., trade associationsc. Firm levelShould the forecast be general or specific (product-wise)?Problems or methods of forecasting for “new” vis-à-vis “well established” products.Classification of products – producer goods, consumer durables, consumer goods, services. Special factors peculiar to the product and the market – risk and uncertainty.

Criteria of a good forecasting method

1. Accuracy – measured by (a) degree of deviations between forecasts and actuals, and (b) the extent of success in forecasting directional changes.

2. Simplicity and ease of comprehension.3. Economy.4. Availability.5. Maintenance of timeliness.

Presentation of a forecast to the Management

1. Make the forecast as easy for the management to understand as possible.

2. Avoid using vague generalities.3. Always pin-point the major assumptions and

sources.4. Give the possible margin of error.5. Omit details about methodology and

calculations.6. Make use of charts and graphs as much as

possible for easy comprehension.

Various macro parameters found useful for demand forecasting

1. National income and per capita income.2. Savings.3. Investment.4. Population growth.5. Government expenditure.6. Taxation.7. Credit policy.

Significance of Demand Forecasting

Production PlanningSales ForecastingControl of BusinessInventory ControlGrowth and Long Term Investment ProgramEconomic Planning and Policy Making

Sources of Data

Primary: which are collected for first time for purpose of analysis

Secondary : are those which are obtained from someone’s else records

Techniques of Demand Forecasting

Techniques of Demand Forecasting

Consumption level

method

Consumer Survey Methods

Complete enumeration Method: All potential users of product are contacted and are asked about their future plan of purchasing the product in question

Limitations Very expensive in case of widely dispersed market Consumers may not know their actual demand and may br

unable to answer query Their plans may change with a change in factors not included

in questionnaire

Contd…

Sample Survey: Only a few potential consumers and users selected from relevant market are surveyed

Method is simpler, less costly and less time consuming.

Surveys are done to understand market demand, tastes ad preferences, Consumer expectations etc

Opinion Poll Method

Aim at collecting opinions of those who are supposed to possess the knowledge of the market e.g sales representatives, sales executives, consultants and professional marketing experts

This method includesExpert opinion Delphi method

Expert opinion

Under this method each expert is asked independently to provide a confidential estimate and results could be averaged.

Experts may include executives directly involved in the market such as suppliers, distributors or dealers or marketing consultants, officers of trade association etc.

Advantage is that there is no danger that group of experts develop a group- think mentality. Moreover, forecasting is done quickly and easily without need of elaborate need of statistics.

Delphi Method

This method is an attempt to arrive at a consensus on some issues by questioning a group of experts repeatedly until the responses appear to converge along a single line or the issues causing disagreement are clearly defined.

Generally a panel consisting 9 to 12 expertsA coordinator is required for the process

Market Experimentation

Test marketing A test area is selected, which should be a representative of the whole

market in which the new product is to be launched.

A test area may include several cities having similar features i.e. population, income levels, cultural and social background, choice and preferences of consumers

Market experiments are carried out by changing prices, advertisement expenditure and other controllable variables influencing demand

After such changes are introduced in the market, consequent changes in demand over a period of time are recorded.

Contd…

Experiments in laboratory or consumer clinic method Under this method consumers are given some money to buy

in a stipulated store goods with varying prices, packages, displays etc.

They are also requested to fill a questionnaire asking reasons for the choices they have made

The experiment reveals the consumers responsiveness to the changes made in prices, packages and displays.

Limitations of market experiment methods

Very expensiveBeing costly, carried out on a scale too small to permit

generalization with a high degree of reliabilityBased on short term and controlled conditions which

may not exist in an uncontrolled marketTinkering with price increases may cause a permanent

loss of customers to competitive brands

Types of data used in Statistical methodsTime series data refer to data collected over a

period of time recording historical changes in price , income and other relevant variables influencing demand for a commodity

Cross sectional analysis is undertaken to determine the effects of changes like price, income etc on demand for a commodity at a point in time

Types of Statistical Methods

Consumption level MethodTime series Analysis (Trend Projection)Smoothing Techniques

Moving AveragesLeast Squares MethodExponential Smoothing Technique

Econometric MethodBarometric Method

Consumption Level Method

Under this method consumption level method may be estimated on basis of co-efficient of Income elasticity and price elasticity of Demand

D* = D(1+M*.e)D* =Projected per capita demandD= Actual Per capita DemandM*= Percentage change in per capita income/priceE=elasticity of demand

Illustration

Suppose Income elasticity of demand for chocolates is 3. In year 1995 per capita income is $500 and per capita annual demand for chocolates is 10 million in a city. It is expected that in year 2000 per capita income will increase by 20 % . Then projected per capita demand for chocolates in 2000 will be?

Time Series Analysis

It attempts to forecast future values of time series by examining past observations of data

The time series relating to sales represent the past pattern of effective demand for a particular product. Such data can be presented either in a tabular form or graphically for further analysis.

The most popular method of analysis of the time series is to project the trend of the time series.a trend line can be fitted through a series either visually or by means of statistical techniques.

The analyst chooses a plausible algebraic relation (linear, quadratic, logarithmic, etc.) between sales and the independent variable, time. The trend line is then projected into the future by extrapolation.

Time Series Analysis

Popular because: simple, inexpensive, time series data often exhibit a persistent growth trend.Disadvantage: this technique yields acceptable results so long as the time series shows a persistent tendency to move in the same direction. Whenever a turning point occurs, however, the trend projection breaks down.The real challenge of forecasting is in the prediction of turning points rather than in the projection of trends.

Time Series Analysis

Reasons for fluctuations in time series dataSecular Trend : value of a variable tends to increase or decrease over a period of timeCyclical Fluctuations are major expansions and contractions that seem to recur every several yearsSeasonal variation refers to regularly recurring fluctuation in economic activity during each yearIrregular influences are variations in data series resulting from wars, natural disasters or other unique eventsFour sets of factors: secular trend (T), seasonal variation (S), cyclical fluctuations (C ), irregular or random forces (I). O (observations) = TSCI

Trend Projection

Simplest form of time series analysis is projecting trend based on assumption that factors responsible for past trends in variable to be projected will remain same in future.

Trends refer to long term persistent movement of data in one direction-increase or decrease

Trend component of time series is the overall direction of the movement of the variable over a long period.

Reasons for studying Trends

Studying secular trends permits us to project past patterns, or trends, into the futureIn many situations studying the secular trend of a time series allows us to eliminate the trend component from the series.Methods for trend Projections:

Least squares method Smoothing TechniquesMoving Average Exponential smoothing

Moving average Method

This method assumes that demand in future year equals the average of demand in past yearsUnder this method 3 yearly,4 or 5 yearly etc moving average is calculated by moving total of values in group of years(3,4,5)is calculated, each time by ignoring first entry and incorporating last one For Three period Moving average the forecasted value of time series for next period is average value of previous three periods in time series

Moving average Method

In order to decide which of these moving averages forecasts is better closer to actual data root-mean-square-error (RMSE) is calculated for each forecast and using moving average that results in smaller RMSEThe greater the number of periods used in moving average the greater is the smoothing effect because each new observation receives less weight. Useful when time series data is more erratic.

Three-quarter Moving Average forecasts

Five Quarter Moving Average forecasts

Three & Five year Moving Average ComparisonRMSE= {(A-F)2 / n}1/2

RMSE = 78.3534/9 = 2.95RMSE = 62.48/7 = 2.99

Thus Three Year Moving Average is marginally better than corresponding Five year

Exponential Smoothing

A serous criticism of using moving averages in forecasting is that they give equal weight to all observations in computing the average even though more recent observations are more importantIt uses a weighted average of past data as basis for a forecast by giving heaviest weight to more recent information and smaller weights to observations in more distant past on assumption that future is more dependent on recent past than on distant past

The value of time series at period t (At) is assigned a weight (w) between 0 and 1 both inclusive, and forecast for period t (Ft) is assigned 1-w . The basic Equation : Ft+1 = wAt + (1-w)Ft

Where Ft+1 = forecast for next period At = Actual value of time t (most recent actual data) Ft = forecast for present period w = weight ie smoothing constant

Contd..

Rules of Thumb:When magnitude of random variations is large, w is taken as lower value so as to even out the effects of random variation quicklyWhen magnitude of random variations is moderate, a large value is assigned to w It has been found appropriate to have w between 0.1 and 0.2 in many systemsTo identify best forecast amongst many arrived from different values of W,RMSE is used and forecast having least RMSE is considered as best

Illustration : Exponential Smoothing

Contd..

Forecast sales of time period 8,9and 10Take a smoothing constant w= 0.2

Econometric Methods

Combine statistical tools with economic theories to estimate economic variables and to forecast intended economic variablesAn econometric model may be a single equation regression model Types of Econometric Method

Regression Method

Regression Method

It attempts to find out relationship between dependent and independent variables It is a statistical technique for obtaining the line that best fits data pointsIt is obtained by minimizing sum of squared vertical deviations of each point from regression line and method used is called Ordinary Least Squares method (OLS)

Contd…

Linear Equation

Y= a +bX Where X and Y are averagesObjective of regression analysis is to estimate linear relationship ie a and b a = Y-bXb = N∑XY – (∑X) (∑Y) N ∑X2 - (∑X)2

Estimating Linear equation

b = 10(10254) – (144)(656) 10(2448) – (144)2

b = 2.15a = Y – bX where Y & X are averagesY = 34.54 + 2.15XIt means that an increase of Rs 1 million in ad expenditure will bring an increase of 2.15 thousand units in sales ie 2,15000 units

Estimating Linear Trend-Least Squares Method

When a time series data reveals rising trend for e.g. in sales then equation is:S= a +bT where a and b are estimated using following two equations∑S= na + b∑T∑ST = a ∑T + b ∑T2

Illustration: Suppose that a local bread manufacturer company wants to assess demand for its product for years 2002,2003 and 2004. for this purpose it uses time series data of its sales over past 10 years.

Estimation of Trend Equation

Contd….

164 = 10a + 55b1024 = 55a + 385bS = 8.26 + 1.48TFor 2002, S2 = 8.26 + 1.48(11) = 24,540 tonnes

Problems: Demand Forecasting

1. Using method of least squares, fit straight line trend and estimate the annual sales of 1997.

Contd..

2. Suppose number of refrigerators sold in past 7 years in a city is given in table. Forecast demand for refrigerator for year 2002 and 2003 by calculating 3-yearly moving average

Contd..

3. Estimate demand for sugar in 2003-04 if population in 2003-04 is projected to be 70 million by using method of least squares to estimate regression equation of form: Y= a+ bXData on Consumption of Sugar:

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